Time-series blood cytokine profiles correlate with treatment responses in triple-negative breast cancer patients.

BACKGROUND: Triple-negative breast cancer (TNBC) is the most heterogeneous breast cancer subtype. Partly due to its heterogeneity, it is currently challenging to stratify TNBC patients and predict treatment outcomes. METHODS: In this study, we examined blood cytokine profiles of TNBC patients throughout treatments (pre-treatment, during chemotherapy, pre-surgery, and 1 year after the surgery in a total of 294 samples). We analyzed the obtained cytokine datasets using weighted correlation network analyses, protein-protein interaction analyses, and logistic regression analyses. RESULTS: We identified five cytokines that correlate with good clinical outcomes: interleukin (IL)-1α, TNF-related apoptosis-inducing ligand (TRAIL), Stem Cell Factor (SCF), Chemokine ligand 5 (CCL5 also known as RANTES), and IL-16. The expression of these cytokines was decreased during chemotherapy and then restored after the treatment. Importantly, patients with good clinical outcomes had constitutively high expression of these cytokines during treatments. Protein-protein interaction analyses implicated that these five cytokines promote an immune response. Logistic regression analyses revealed that IL-1α and TRAIL expression levels at pre-treatment could predict treatment outcomes in our cohort. CONCLUSION: We concluded that time-series cytokine profiles in breast cancer patients may be useful for understanding immune cell activity during treatment and for predicting treatment outcomes, supporting precision medicine. TRIAL REGISTRATION: The study has been registered with the University Hospital Medical Information Network Clinical Trials Registry ( http://www.umin.ac.jp/ctr/index-j.htm ) with the unique trial number UMIN000023162. The association Japan Breast Cancer Research Group trial number is JBCRG-22. The clinical outcome of the JBCRG-22 study was published in Breast Cancer Research and Treatment on 25 March 2021. https://doi.org/10.1007/s10549-021-06184-w .

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning.

Background: Phenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge. Methods: We recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms. Results: Participants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep-wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit. Conclusion: We found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery.

Fabricating Muscle-Neuron Constructs with Improved Contractile Force Generation.

IMPACT STATEMENT: In this study, we fabricated innervated skeletal muscle tissue constructs comprising C2C12 myoblasts and PC12 neural cells using a magnetic force-based tissue engineering technique. We found that the C2C12/PC12 co-culture enhanced neural differentiation of PC12 cells and sarcomere formation of C2C12 myotubes, accompanying with neuromuscular junction formation. The innervated skeletal muscle tissue constructs generated significantly higher contractile forces compared with aneural (C2C12 monoculture) skeletal muscle tissue constructs. These innervated skeletal muscle tissue constructs can be a useful tool for drug testing and biological research for neuromuscular diseases.

Effects of heat stimulation and l-ascorbic acid 2-phosphate supplementation on myogenic differentiation of artificial skeletal muscle tissue constructs.

Although skeletal muscle tissue engineering has been extensively studied, the physical forces produced by tissue-engineered skeletal muscles remain to be improved for potential clinical utility. In this study, we examined the effects of mild heat stimulation and supplementation of a l-ascorbic acid derivative, l-ascorbic acid 2-phosphate (AscP), on myoblast differentiation and physical force generation of tissue-engineered skeletal muscles. Compared with control cultures at 37°C, mouse C2C12 myoblast cells cultured at 39°C enhanced myotube diameter (skeletal muscle hypertrophy), whereas mild heat stimulation did not promote myotube formation (differentiation rate). Conversely, AscP supplementation resulted in an increased differentiation rate but did not induce skeletal muscle hypertrophy. Following combined treatment with mild heat stimulation and AscP supplementation, both skeletal muscle hypertrophy and differentiation rate were enhanced. Moreover, the active tension produced by the tissue-engineered skeletal muscles was improved following combined treatment. These findings indicate that tissue culture using mild heat stimulation and AscP supplementation is a promising approach to enhance the function of tissue-engineered skeletal muscles. Copyright © 2015 John Wiley & Sons, Ltd.

Development of a Support Vector Machine-Based System to Predict Whether a Compound Is a Substrate of a Given Drug Transporter Using Its Chemical Structure.

The aim of this study was to develop an in silico prediction system to assess which of 7 categories of drug transporters (organic anion transporting polypeptide [OATP] 1B1/1B3, multidrug resistance-associated protein [MRP] 2/3/4, organic anion transporter [OAT] 1, OAT3, organic cation transporter [OCT] 1/2/multidrug and toxin extrusion [MATE] 1/2-K, multidrug resistance protein 1 [MDR1], and breast cancer resistance protein [BCRP]) can recognize compounds as substrates using its chemical structure alone. We compiled an internal data set consisting of 260 compounds that are substrates for at least 1 of the 7 categories of drug transporters. Four physicochemical parameters (charge, molecular weight, lipophilicity, and plasma unbound fraction) of each compound were used as the basic descriptors. Furthermore, a greedy algorithm was used to select 3 additional physicochemical descriptors from 731 available descriptors. In addition, transporter nonsubstrates tend not to be in the public domain; we, thus, tried to compile an expert-curated data set of putative nonsubstrates for each transporter using personal opinions of 11 researchers in the field of drug transporters. The best prediction was finally achieved by a support vector machine based on 4 basic and 3 additional descriptors. The model correctly judged that 364 of 412 compounds (internal data set) and 111 of 136 compounds (external data set) were substrates, indicating that this model performs well enough to predict the specificity of transporter substrates.

Improved contractile force generation of tissue-engineered skeletal muscle constructs by IGF-I and Bcl-2 gene transfer with electrical pulse stimulation.

INTRODUCTION: Tissue-engineered skeletal muscle constructs should be designed to generate contractile force with directional movement. Because electrical impulses from a somatic nervous system are crucial for in vivo skeletal muscle development, electrical pulse stimulation (EPS) culture as an artificial exercise is essential to fabricate functional skeletal muscle tissues in vitro. To further improve muscle functions, the activation of cell-signaling pathways from myogenic growth factors, such as insulin-like growth factor (IGF)-I, is also important. Because tissue-engineered skeletal muscle constructs should maintain a high cell-dense structure, the expression of an anti-apoptotic factor, such as B-cell lymphoma 2 (Bcl-2), could be effective in preventing cell death. METHODS: In the present study, myoblasts were genetically modified with inducible expression units of IGF-I and Bcl-2 genes, and the tissue-engineered skeletal muscle constructs fabricated from the myoblasts were cultured under continuous EPS. RESULTS: Overexpression of IGF-I gene induced muscular hypertrophy in the muscle tissue constructs, and Bcl-2-overexpressing myoblasts formed significantly cell-dense and viable muscle tissue constructs. Furthermore, the combination of IGF-I and Bcl-2 gene transfer with EPS culture highly improved the force generation of the tissue-engineered skeletal muscle constructs. CONCLUSIONS: This approach has the potential to yield functional skeletal muscle substitutes with high force generation ability.

Shootin1-cortactin interaction mediates signal-force transduction for axon outgrowth.

Motile cells transduce environmental chemical signals into mechanical forces to achieve properly controlled migration. This signal-force transduction is thought to require regulated mechanical coupling between actin filaments (F-actins), which undergo retrograde flow at the cellular leading edge, and cell adhesions via linker "clutch" molecules. However, the molecular machinery mediating this regulatory coupling remains unclear. Here we show that the F-actin binding molecule cortactin directly interacts with a clutch molecule, shootin1, in axonal growth cones, thereby mediating the linkage between F-actin retrograde flow and cell adhesions through L1-CAM. Shootin1-cortactin interaction was enhanced by shootin1 phosphorylation by Pak1, which is activated by the axonal chemoattractant netrin-1. We provide evidence that shootin1-cortactin interaction participates in netrin-1-induced F-actin adhesion coupling and in the promotion of traction forces for axon outgrowth. Under cell signaling, this regulatory F-actin adhesion coupling in growth cones cooperates with actin polymerization for efficient cellular motility.